Computational Intelligence for Genomics Data
- 1st Edition - January 21, 2025
- Imprint: Academic Press
- Editors: Babita Pandey, Valentina Emilia Balas, Suman Lata Tripathi, Devendra Kumar Pandey, Mufti Mahmud
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 3 0 0 8 0 - 6
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 3 0 0 8 1 - 3
Computational Intelligence for Genomics Data presents an overview of machine learning and deep learning techniques being developed for the analysis of genomic data and the develo… Read more
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Computational Intelligence for Genomics Data presents an overview of machine learning and deep learning techniques being developed for the analysis of genomic data and the development of disease prediction models. The book focuses on machine and deep learning techniques applied to dimensionality reduction, feature extraction, and expressive gene selection. It includes designs, algorithms, and simulations on MATLAB and Python for larger prediction models and explores the possibilities of software and hardware-based applications and devices for genomic disease prediction. With the inclusion of important case studies and examples, this book will be a helpful resource for researchers, graduate students, and professional engineers.
- Provides comparative analysis of machine learning and deep learning methods in the analysis of genomic data, discussing major design challenges, best practices, pitfalls, and research potential
- Explores machine and deep learning techniques applied to dimensionality reduction, feature extraction, data selection, and their application in genomics
- Presents case studies of various diseases based on gene microarray expression data, including cancer, liver disorders, neuromuscular disorders, and neurodegenerative disorders
Graduate students, researchers, and professionals in the fields of artificial intelligence, computational modeling, data analysis, and bioinformatics
Section 1: Introduction to biological data and analysis
1.1 Genomic data
1.2 Microarray analysis
1.3 Hub gene selection
1.4 Pathogenesis
1.5 Expressive gene
1.6 Gene reduction
1.7 Biomarkers
Section 2: Traditional Machine learning models for gene selection and classification
2.1 Gene selection and liver disease classification using machine learning
2.2 Gene selection and Diabetic kidney disease classification using machine learning
2.3. Gene selection and neurodegenerative disease classification using machine learning
2.4. Gene selection and neuromuscular disorder classification using machine learning
2.5. Gene selection and cancer classification using machine learning
2.6. Gene selection and disease classification using machine learning
Section3: Deep learning models for gene selection and classification
3.1 Gene selection and liver disease classification using deep learning
3.2 Gene selection and Diabetic kidney disease classification using machine learning
3.3. Gene selection and neurodegenerative disease classification using deep learning
3.4. Gene selection and neuromuscular disorder classification using deep learning
3.5. Gene selection and cancer classification using deep learning
3.6. Gene selection and disease classification using deep learning
Section 4: Gene selection and classification using Artificial intelligence-based optimization methods
4.1 Gene selection and liver disease classification using Particle warm optimization, genetic algorithm, principal component analysis, wolf optimization, ant colony optimization etc.
4.2 Gene selection and Diabetic kidney disease classification using Particle warm optimization, genetic algorithm, principal component analysis, wolf optimization, ant colony optimization etc.
4.3. Gene selection and neurodegenerative disease classification using Particle warm optimization, genetic algorithm, principal component analysis, wolf optimization, ant colony optimization etc.
4.4. Gene selection and neuromuscular disorder classification using Particle warm optimization, genetic algorithm, principal component analysis, wolf optimization, ant colony optimization etc.
4.5 Gene selection and cancer classification using Particle warm optimization, genetic algorithm, principal component analysis, wolf optimization, ant colony optimization etc.
Section 5: Explainable AI for computational biology
5.1. Use of LIME for diagnosis of disease
5.2. Use of Shape for diagnosis of disease
5.3. Quantitative graph theory for integrated omics data
Section 6: Applications of computational biology in healthcare
6.1 Diagnosis of liver disorder
6.2 Diagnosis of diabetic kidney disease
6.3 Diagnosis of cancer
6.4 Diagnosis of neurodegenerative disorder.
6.5 Diagnosis of neuromuscular disorder
6.6. Diagnosis of any other health disorder
1.1 Genomic data
1.2 Microarray analysis
1.3 Hub gene selection
1.4 Pathogenesis
1.5 Expressive gene
1.6 Gene reduction
1.7 Biomarkers
Section 2: Traditional Machine learning models for gene selection and classification
2.1 Gene selection and liver disease classification using machine learning
2.2 Gene selection and Diabetic kidney disease classification using machine learning
2.3. Gene selection and neurodegenerative disease classification using machine learning
2.4. Gene selection and neuromuscular disorder classification using machine learning
2.5. Gene selection and cancer classification using machine learning
2.6. Gene selection and disease classification using machine learning
Section3: Deep learning models for gene selection and classification
3.1 Gene selection and liver disease classification using deep learning
3.2 Gene selection and Diabetic kidney disease classification using machine learning
3.3. Gene selection and neurodegenerative disease classification using deep learning
3.4. Gene selection and neuromuscular disorder classification using deep learning
3.5. Gene selection and cancer classification using deep learning
3.6. Gene selection and disease classification using deep learning
Section 4: Gene selection and classification using Artificial intelligence-based optimization methods
4.1 Gene selection and liver disease classification using Particle warm optimization, genetic algorithm, principal component analysis, wolf optimization, ant colony optimization etc.
4.2 Gene selection and Diabetic kidney disease classification using Particle warm optimization, genetic algorithm, principal component analysis, wolf optimization, ant colony optimization etc.
4.3. Gene selection and neurodegenerative disease classification using Particle warm optimization, genetic algorithm, principal component analysis, wolf optimization, ant colony optimization etc.
4.4. Gene selection and neuromuscular disorder classification using Particle warm optimization, genetic algorithm, principal component analysis, wolf optimization, ant colony optimization etc.
4.5 Gene selection and cancer classification using Particle warm optimization, genetic algorithm, principal component analysis, wolf optimization, ant colony optimization etc.
Section 5: Explainable AI for computational biology
5.1. Use of LIME for diagnosis of disease
5.2. Use of Shape for diagnosis of disease
5.3. Quantitative graph theory for integrated omics data
Section 6: Applications of computational biology in healthcare
6.1 Diagnosis of liver disorder
6.2 Diagnosis of diabetic kidney disease
6.3 Diagnosis of cancer
6.4 Diagnosis of neurodegenerative disorder.
6.5 Diagnosis of neuromuscular disorder
6.6. Diagnosis of any other health disorder
- Edition: 1
- Published: January 21, 2025
- Imprint: Academic Press
- Language: English
BP
Babita Pandey
Babita Pandey completed her PhD in the field of artificial intelligence in biomedical signal processing from IIT-BHU, Varanasi. Currently, she is an Associate Professor of Computer Science at Babasaheb Bhimrao Ambedkar University, Lucknow. She has published more than 150 research papers in refereed journals and conferences. She has delivered many expert lectures for students as well as in faculty development programs. She has worked as a session chair, conference steering committee member, editorial board member, and peer reviewer in various international/national conferences. She received the “Research Excellence Award” in 2014, 2015, 2016 from Lovely Professional University and the “Research and Academic Excellence Award” in 2021, 2022 at Babasaheb Bhimrao Ambedkar University, Lucknow, India. She also received the P.D. Sethi Award 2022. She edited one book on link prediction in social networks. Her areas of research include link prediction, dimensionality reduction of genomic data, e-learning, machine learning and deep learning deployed for images.
Affiliations and expertise
Babasaheb Bhimrao Ambedkar University, Lucknow, IndiaVE
Valentina Emilia Balas
Valentina Emilia Balas is currently a Full Professor in the Department of Automatics and Applied Software at the Faculty of Engineering, “Aurel Vlaicu” University of Arad, Romania. She holds a PhD cum Laude in Applied Electronics and Telecommunications from the Polytechnic University of Timisoara. Dr. Balas is the author of more than 350 research papers. She is the Editor-in-Chief of the 'International Journal of Advanced Intelligence Paradigms' and the 'International Journal of Computational Systems Engineering', an editorial board member for several other national and international publications, and an expert evaluator for national and international projects and PhD theses.
Affiliations and expertise
Full Professor, Department of Automatics and Applied Software, Faculty of Engineering, "Aurel Vlaicu" University of Arad, Arad, RomaniaST
Suman Lata Tripathi
Suman Lata Tripathi completed her PhD in the area of microelectronics and VLSI from MNNIT, Allahabad. She was also a remote post-doc researcher at Nottingham Trent University, London, UK in 2022. She is a Professor at Lovely Professional University with more than 19 years of experience in academics. She has published more than 89 research papers in refereed journals and conferences. She has also published 13 Indian patents and 2 copyrights. She has organized several workshops, summer internships, and expert lectures for students. She has worked as a session chair, conference steering committee member, editorial board member, and peer reviewer in international/national conferences. She received the “Research Excellence Award” in 2019 and “Research Appreciation Award” in 2020, 2021 at Lovely Professional University, India. She also received funded projects from SERB DST under the scheme TARE in the area of Microelectronics devices. She has edited or authored more than 15 books in different areas of Electronics and electrical engineering. Her areas of expertise includes microelectronics device modeling and characterization, low power VLSI circuit design, VLSI design of testing, and advanced FET design for IoT, Embedded System Design, reconfigurable architecture with FPGAs and biomedical applications.
Affiliations and expertise
Lovely Professional University, Phagwara, Punjab, IndiaDP
Devendra Kumar Pandey
Devendra Kumar Pandey completed his PhD in Bioprocess Engineering from IIT (BHU), Varanasi. He has been a Professor at Lovely Professional University for more than 22 years. He has published more than 112 research papers in refereed journals and conferences. He received the “Research Excellence Award” in 2019 and “Research Appreciation Award” in 2020, 2021 at Lovely Professional University, India. He has also received funded projects from UPCST under the area of Medicinal Plants. His areas of expertise include Bioprocess Engineering, Medicinal Plant Biotechnology, Plant-Microbe interaction, Computational Biology.
Affiliations and expertise
Lovely Professional University, Phagwara, Punjab, IndiaMM
Mufti Mahmud
Mufti Mahmud is an Associate Professor of Cognitive Computing at the Department of Computer Science of Nottingham Trent University (NTU). Dr. Mahmud was appointed to the USET, University Shadow Executive Team, in 2022, providing specialist input to the University Executive Team and Vice-Chancellor on strategic policy and direction matters related to Equality, Diversity & Inclusion (EDI). He is the Coordinator of the Computer Science and Informatics Unit of Assessment of Research Excellence Framework at NTU and the deputy group leader of the Interactive Systems Research Group (ISRG) and the Cognitive Computing & Brain Informatics (CCBI) research group. He is also an active member of the Computing and Informatics Research Centre (CIRC) and the Medical Technologies Innovation Facility (MTIF). He is a member of the NTU Distance Learning Governance, Operation and Steering committee as well as the International Mobility Committee and serves as an independent end-point assessor for the Level 6 BSc (Hons) in Digital & Technology Solutions Professional Degree Apprenticeship, and an expert of the online master's degree in computer science. He led the teaching of the Big Data and its Infrastructures (Postgraduate – on-campus and online delivery) module. He is a Fellow of the Higher Education Academy, a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE) and the Association of Computing Machinery (ACM), and a professional member of the British Computer Society (BCS).
Affiliations and expertise
Department of Computer Science and Engineering, Nottingham Trent University, UKRead Computational Intelligence for Genomics Data on ScienceDirect